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Adam C. Sales; Lora Dufresne; Anzhe Tao; Sean Sullivan – Society for Research on Educational Effectiveness, 2025
Background: One of the most vexing threats to education field trials is attrition--when some subjects drop out before it is complete. Since outcomes are not available for subjects who attrit, they are typically dropped from any analysis estimating average effects on the outcome. However, since attrition may itself have been affected by treatment…
Descriptors: Randomized Controlled Trials, Attrition (Research Studies), Educational Research, Computation
Huibin Zhang; Zuchao Shen; Walter L. Leite – Journal of Experimental Education, 2025
Cluster-randomized trials have been widely used to evaluate the treatment effects of interventions on student outcomes. When interventions are implemented by teachers, researchers need to account for the nested structure in schools (i.e., students are nested within teachers nested within schools). Schools usually have a very limited number of…
Descriptors: Sample Size, Multivariate Analysis, Randomized Controlled Trials, Correlation
Peter Z. Schochet – Journal of Educational and Behavioral Statistics, 2025
Random encouragement designs evaluate treatments that aim to increase participation in a program or activity. These randomized controlled trials (RCTs) can also assess the mediated effects of participation itself on longer term outcomes using a complier average causal effect (CACE) estimation framework. This article considers power analysis…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
Regan Mozer; Luke Miratrix – Grantee Submission, 2025
For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to…
Descriptors: Artificial Intelligence, Coding, Efficiency, Statistical Inference
Charlotte Z. Mann; Adam C. Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2025
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…
Descriptors: Causal Models, Statistical Analysis, Privacy, Risk
Martin Brunner; Sophie E. Stallasch; Cordula Artelt; Oliver Lüdtke – Educational Psychology Review, 2025
There is a need for robust evidence about which educational interventions work in preschool to foster children's cognitive and socio-emotional learning (SEL) outcomes. Lab-based individually randomized experiments can develop and refine such interventions, and field-based randomized experiments (e.g., cluster randomized trials) evaluate their…
Descriptors: Preschools, Social Emotional Learning, Outcomes of Education, Cognitive Objectives
Chun-Wei Huang; Linlin Li; Mingyu Feng – Society for Research on Educational Effectiveness, 2025
Background: An efficacy study based on a randomized controlled trial (RCT) was designed to evaluate the impact of ASSISTments on math learning among middle school students, including those from high-needs rural schools. Despite extensive recruitment efforts in 2021 (during the pandemic), only a few schools and their teachers agreed to participate,…
Descriptors: Educational Technology, Feedback (Response), Formative Evaluation, Middle School Students
Chun-Wei Huang; Mingyu Feng; Linlin Li – WestEd, 2025
This is a technical report detailing the analytic methods used to assess the effects of a virtual professional learning community (vPLC)-enhanced ASSISTments intervention. The report uses a quasi-experimental design and partially nested regression model to account for clustering and reveals that while overall effects were positive but not…
Descriptors: Educational Technology, Feedback (Response), Formative Evaluation, Middle School Students

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